VISUALIZATIONS OF QUERY RESULTS USING GENERATED FILES

    公开(公告)号:US20250068639A1

    公开(公告)日:2025-02-27

    申请号:US18827113

    申请日:2024-09-06

    Applicant: Splunk Inc.

    Abstract: Systems and methods are disclosed for generating one or more files to visualize query results. The systems and methods can include parsing one or more files that include one or more queries and computer-executable instructions for displaying results of the one or more queries. The one or more queries can identify a set of data to be processed and a manner of processing the set of data. The systems and methods can further include generating one or more files that include the results of the queries and computer-executable instructions for displaying one or more visualizations of the results.

    QUERY EXECUTION USING A DATA PROCESSING SCHEME OF A SEPARATE DATA PROCESSING SYSTEM

    公开(公告)号:US20250028714A1

    公开(公告)日:2025-01-23

    申请号:US18428405

    申请日:2024-01-31

    Applicant: Splunk Inc.

    Abstract: A query coordinator can receive a query. The query coordinator can determine one or more data semantics of a first data processing system. The data semantics of the first data processing system may be based on execution of one or more queries by the first data processing system. The query coordinator can define a query processing scheme for obtaining and processing data based on the query. The query processing scheme may include instructions for a second data processing system to execute at least a portion of the query according to the data semantics of the first data processing system. The query coordinator can provide the query processing scheme to the second data processing system and obtain query results from the second data processing system.

    Techniques for visualizing browser test metrics

    公开(公告)号:US12204437B1

    公开(公告)日:2025-01-21

    申请号:US18104212

    申请日:2023-01-31

    Applicant: SPLUNK Inc.

    Abstract: Techniques, which may be embodied herein as systems, computing devices, methods, algorithms, software, code, computer readable media, or the like, are described herein for comparing a set of metrics generated during a simulated user interaction with a website to metrics generated by observing real user interactions with the website. Simulated user interactions with a website can be used to diagnose a website's performance issues, but it can be difficult to determine whether the simulated interactions reflect the experience of real users. In addition, the simulated user interactions can be challenging to contextualize because the number of observed real user interactions may significantly outnumber the simulated interactions. A graphical user interface can help with the interpretation of these website interactions by using the real user interactions to properly contextualize the simulated results.

    Exploratory data analysis system for generation of wildcards within log templates through log clustering and analysis thereof

    公开(公告)号:US12182174B1

    公开(公告)日:2024-12-31

    申请号:US18147639

    申请日:2022-12-28

    Applicant: SPLUNK Inc.

    Abstract: A search assistant engine is described that integrates with a data intake and query system and provides an intuitive user interface to assist a user in searching and evaluating indexed event data. Additionally, the search assistant engine provides logic to intelligently provide data to the user through the user interface such as determining fields of events likely to be of interest based on determining a mutual information score for each field and determining groups of related fields based on determining a mutual information score for each field grouping. Some implementations utilize machine learning techniques in certain analyses such as when clustering events and determining an event templates for each cluster. Additionally, the search assistant engine may import terms or characters from user interaction into predetermined search query templates to generate tailored search query for the user.

    Systems and methods for machine-learning based alert grouping including temporal constraints

    公开(公告)号:US12182169B1

    公开(公告)日:2024-12-31

    申请号:US17589600

    申请日:2022-01-31

    Applicant: Splunk, Inc.

    Abstract: A computerized method is disclosed for grouping alerts through machine learning while implementing certain time constraints. The method includes receiving an alert to be assigned to any of a plurality of existing issues or to a newly created issue, the alert including a temporal field that includes a timestamp of an arrival time of the alert, wherein an issue is a grouping of one or more alerts, determining a subset of existing issues from the plurality of existing issues that each satisfy time constraints, wherein the time constraints correspond to (i) a time elapsed between a most recent alert of a first existing issue and a timestamp of the alert, or (ii) a maximum issue time length of the first existing issue, and deploying a trained machine learning model to assign the alert to either an existing issue of the subset of existing issues or a newly created issue.

    Machine-learning based prioritization of alert groupings

    公开(公告)号:US12181956B1

    公开(公告)日:2024-12-31

    申请号:US18208879

    申请日:2023-06-12

    Applicant: Splunk Inc.

    Abstract: Systems and methods are disclosed that are directed to improving the prioritization, display, and viewing of system alerts through the use of machine learning techniques to group the alerts and further to prioritize the groupings. Additionally, a graphical user interface is generated that illustrates the prioritized listing of the plurality of groupings. Thus, a system administrator or other user receives an improved experience as the number of notifications provided to the system administrator are reduced due to the grouping of individual alerts into related groupings and further due to the prioritization of the groupings. Previously, or in current technology, system alerts may be automatically generated and provided immediately to a system administrator. In some instances, any advantage of detecting system errors or system monitoring provided by the alerts is negated by the vast number of alerts and provision of minimally important alerts in a manner that concealed more important alerts.

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